Topic: [The Neural Basis of Loss Aversion in Decision-Making Under Risk]

This repository attempts to reproduce the original analysis on "The Neural Basis of Loss Aversion in Decision-Making Under Risk" done by Sabrina M. Tom, Craig R. Fox, Christopher Trepel, Russell A. Poldrack. The imaging data were collected using the fMRI method. They were processed and analyzed in order to identify the regions of the brain activated by the decision making process. This study also investigated the relationship between the brain activity and the behavior of the subjects towards the gambling situations using a whole-brain robust regression analysis. Please follow the insturctions to explore on the repository.

1. Clone the repo: `git clone https://github.com/berkeley-stat159/project-epsilon.git'
2. Install python dependencies with pip: pip install -r requirements.txt

• Data make data : Downloads the ds005 dataset including brain scan images of total 16 subjects. When rin from this repository, this commend will download the raw data and the filerted data provided. The total size of the file is ~17GB.

• Validate make validate_data : Validates the downloaded data

• Clean make clean : remove compiled python files

• Test make test : Tests the functions in code/utils folder

• Coverage make coverage : Creates a coverage report for the functions in code/utils/ folder

• Verbose make verbose : Tests the functions in code/utils folder via nosetests option

• Report make report : Creates final_report.pdf under paper/ and clean the paper/ directory

• Analysis for Subject 1 and 5 make analysis-except-multi : Executes all analysis ( except for the multi comparison) and creates relevant img files under fig/ folder

  • NOTICE : make multi-comparison will run about for 1 hour because it has to generate all the beta values for each single voxel for each subject over time-course.

• To make all analyses make analysis-except-multi and then make multi-comparison

• Each of analysis :

  • make eda
  • make linear
  • make logistic
  • make convolution-high
  • make convolution-normal
  • make t-test
  • make glm
  • make noise-pca
  • make multi-comparison

• If you want to perform each analysis, please be aware of the following dependencies:

  • linear (prerequisites: convolution-normal, convolution-high)
  • logistic (prerequisites: convolution-normal, convolution-high)
  • t-test (prerequisites: convolution-normal, convolution-high)
  • glm (prerequesites: convolution-normal, convolution-high)
  • noise-pca (prerequisites: convolution-normal, convolution-high)
  • multi-comparison (prerequisites: convolution-normal, convolution-high, glm)

###Special note for Ross

• Please use make filtered_data_only to complete the download of the filtered data before runnning : make analysis-except-multi and then make multi-comparison

• Min Gu Jo (mingujo)
• Soazig Kaam (soazig)
• Zhuangdi Li (lizhua)
• Ye Zhi (ye-zhi)
• Timothy Yu (timothy1191xa)